MLOps community meetup #64! Last Wednesday we talked to Christopher Bergh, CEO, DataKitchen.

//Abstract
Working on a shared technically difficult problem there will be some things that are important no matter what industry you are in. Whether it’s building cars in a factory, using agile or scrum methodology, or productionizing ML models you need a few basics. Chris gives us some of his best practices in the conversation.

//Bio
Chris Bergh is the CEO and Head Chef at DataKitchen. Chris has more than 25 years of research, software engineering, data analytics, and executive management experience. At various points in his career, he has been a COO, CTO, VP, and Director of Engineering. Chris is a recognized expert on DataOps. He is the co-author of the “DataOps Cookbook” and the “DataOps Manifesto,” and a speaker on DataOps at many industry conferences.

//Takeaways
Your model is not an island. For success, Data science requires a high level of technical collaboration with other parts of the data organization.

//Other Links
On-Demand Webinar – Your Model is Not an Island: Operationalizing Machine Learning at Scale with ModelOps
https://info.datakitchen.io/watch-on-demand-webinar-operationalize-machine-learning-at-scale-with-modelops

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Connect with Demetrios on LinkedIn: https://www.linkedin.com/in/dpbrinkm/
Connect with Chris on LinkedIn: https://www.linkedin.com/in/chrisbergh/

Timestamps:
[00:00] Introduction to Christopher Bergh
[02:57] MLOps community in partnership with MLOps World Conference
[04:34] Chris’ Background
[07:59] “When we started with the company, I realized that the problem I have is generalizable to everyone. I’m getting enough there in years and I wanted to remove the amount of pain that other people have.”
[09:53] DataOps vs MLOps
[10:15] “I don’t really honestly care what Ops you use, right? Hahaha! Call it your favorite Ops ’cause first of all as an engineer, I want precise definitions. I look at it from a completely odd-ball way so you could call it whatever Ops term you want.”
[12:45] Best practices of companies
[14:16] “When that code runs in production, monitor and check to see if it’s right. Absorb it, monitor it because the model could go out of tune. The data going into it could be wrong. The data transformation could break. Shit happens and don’t trust your data providers.”
[19:00] The whole is still greater than its part
[20:26] “It is harder to focus on the results than just under a piece of the task. Don’t spend too much time on doing the wrong thing.”
[23:50] DevOps Principles and Agile
[26:19] “Any good engineering wisdom is true no matter what language you put it in. This is just good common sense. Build a little, test a little, learn a lot.”
[27:17] DataOps Manifesto – DataOps is Data Management reborn
[27:45] “The ‘Ops’ term is ending up encompassing the work that you do in addition to the system you build to do the work.”
[30:45] Standardization
[32:22] “I think that there’s a lack of perception of the need to spend time on doing the operations part of the equation.”
[34:15] Tools as lego blocks
[34:49] “Good interphases make good neighbors.”
[36:23] “Standards can help but they’re not the panacea.”
[36:30] Cultural side – You build it, you own it, you ship it
[37:35] “I think you need to have a shared obstruction between all those people that they can understand in a technical way where they have a relationship to each other. Pull the pain forward.”
[39:28] Value chain
[42:08] “You want yourself to be replaceable because that means you can move on to different things.”
[44:19] Ripple effect of testing
[47:04] “In production, your data is varying but your code is fixed. In development, your data is fixed but your code is varying.”
[48:03] Google on “One tool to rule them all”
[49:50] “Legacy happens if you’re gonna live in the real world and not start greenfield projects.”
[53:47] Starting MLOps in the legacy system
[54:00] “Every time you have a problem, big or small, write it down. And then once a month, sit down with some people, look at the list and prioritize one thing that you’re gonna fix. When you fix it, do it in an automated scripture in a way so it doesn’t happen again.”
[54:25] “Write some tests and have those tests run the manual development cycle every time and run them in production. You don’t need a tool to do that. But that’s on the line, you wanna give 15 percent of your time to these ops activities.”
[56:13] “You’re not successful when you’re working nights and weekends. Hope and heroism should only happen a little best.”

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